Abstract: User-specific neurostimulation settings are efficiently determined and optimized based on an optimized set of population-based neurostimulation settings. The population data are clustered and a set of test settings for a new user are selected as settings that efficiently discriminate between the clusters. User preference of the test settings are used to map the user to a particular cluster of settings, which can be used to determine user-specific neurostimulation settings. The user-based settings can be iteratively updated and/or optimized using information from the population data, such as by using average preference score surfaces in the population data to identify and/or filter new test settings for the user.

Abstract: Neurostimulation, such as electrical and/or magnetic neurostimulation, is controlled using an adaptive real-time state space (ARTISTS) control framework to determine and/or adjust stimulation settings (e.g., stimulation waveforms). The ARTISTS control framework generally includes an adaptive autoregressive model of the nervous system's response to stimulation, an amended Kalman filter to estimate the state and coefficients of the autoregressive model, and a linear quadratic regulator to determine the stimulation waveform to be delivered.

Abstract: A medical device is controlled based in part on a Bayesian preference learning-based optimization of the control parameters of the device. The Bayesian preference learning-based optimization is implemented to identify personalized optimal control parameters based on user preference for control parameter settings. The Bayesian preference learning-based optimization provides automatic tuning of the control parameters of the medical device based on feedback data, such as user response data, to achieve a user-specific therapy or effect.

Abstract: User-specific neurostimulation settings are efficiently determined and optimized based on an optimized set of population-based neurostimulation settings. The population data are clustered and a set of test settings for a new user are selected as settings that efficiently discriminate between the clusters. User preference of the test settings are used to map the user to a particular cluster of settings, which can be used to determine user-specific neurostimulation settings. The user-based settings can be iteratively updated and/or optimized using information from the population data, such as by using average preference score surfaces in the population data to identify and/or filter new test settings for the user.